Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer

Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on th...

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Vydáno v:PloS one Ročník 16; číslo 11; s. e0260232
Hlavní autoři: Elkabbash, Emad T., Mostafa, Reham R., Barakat, Sherif I.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 19.11.2021
Public Library of Science (PLoS)
Témata:
Accuracy
Algorithms
Analysis
Application programming interface
Benchmarking - methods
Biology and Life Sciences
Business metrics
Classification
Computer and Information Sciences
Computer Security
Detectors
Engineering and Technology
Evaluation
Feature selection
Heuristic methods
Identification and classification
Information science
Information systems
Learning algorithms
Machine Learning
Malware
Market shares
Methyltestosterone
Mobile operating systems
Operating systems
Optimization
Performance measurement
Physical Sciences
Research and Analysis Methods
Smartphone - instrumentation
Smartphones
Software
Source code
Spyware
Theft
Egypt
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0260232